{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SLTJ6T4WRUYG5YUXVXXNHZ4D72","short_pith_number":"pith:SLTJ6T4W","schema_version":"1.0","canonical_sha256":"92e69f4f968d306ee297adeed3e783fea360e1dca7e300164fa49c83d969c1b9","source":{"kind":"arxiv","id":"1803.01160","version":3},"attestation_state":"computed","paper":{"title":"Real-Time Deep Learning Method for Abandoned Luggage Detection in Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Radu Tudor Ionescu, Sorina Smeureanu","submitted_at":"2018-03-03T13:30:06Z","abstract_excerpt":"Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens. One potential threat is represented by abandoned luggage items (that could contain bombs or biological warfare) in public areas. In this paper, we describe an approach for real-time automatic detection of abandoned luggage in video captured by surveillance cameras. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). T"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1803.01160","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-03T13:30:06Z","cross_cats_sorted":[],"title_canon_sha256":"b3fbc5a969f0d1f2aed1e0a5436eed5a8472f981c34fa2d32ea03ce4a2e7425b","abstract_canon_sha256":"82533e2cb4c6f2a4967370f4cbb3200e39b048370ecdcb3f90e1a4e9852e0435"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:10.347127Z","signature_b64":"e/6z3u509m3g8oRdddVbqwBCfgfT3tEtNt5qbMFYmNLoTUnr8Z5T8yuU5pc+kGRtsuy8o7T77uQ0WuUJdtGMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92e69f4f968d306ee297adeed3e783fea360e1dca7e300164fa49c83d969c1b9","last_reissued_at":"2026-05-18T00:13:10.346443Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:10.346443Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Real-Time Deep Learning Method for Abandoned Luggage Detection in Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Radu Tudor Ionescu, Sorina Smeureanu","submitted_at":"2018-03-03T13:30:06Z","abstract_excerpt":"Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens. One potential threat is represented by abandoned luggage items (that could contain bombs or biological warfare) in public areas. In this paper, we describe an approach for real-time automatic detection of abandoned luggage in video captured by surveillance cameras. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01160","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1803.01160","created_at":"2026-05-18T00:13:10.346536+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.01160v3","created_at":"2026-05-18T00:13:10.346536+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01160","created_at":"2026-05-18T00:13:10.346536+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLTJ6T4WRUYG","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLTJ6T4WRUYG5YUX","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLTJ6T4W","created_at":"2026-05-18T12:32:53.628368+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72","json":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72.json","graph_json":"https://pith.science/api/pith-number/SLTJ6T4WRUYG5YUXVXXNHZ4D72/graph.json","events_json":"https://pith.science/api/pith-number/SLTJ6T4WRUYG5YUXVXXNHZ4D72/events.json","paper":"https://pith.science/paper/SLTJ6T4W"},"agent_actions":{"view_html":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72","download_json":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72.json","view_paper":"https://pith.science/paper/SLTJ6T4W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.01160&json=true","fetch_graph":"https://pith.science/api/pith-number/SLTJ6T4WRUYG5YUXVXXNHZ4D72/graph.json","fetch_events":"https://pith.science/api/pith-number/SLTJ6T4WRUYG5YUXVXXNHZ4D72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72/action/storage_attestation","attest_author":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72/action/author_attestation","sign_citation":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72/action/citation_signature","submit_replication":"https://pith.science/pith/SLTJ6T4WRUYG5YUXVXXNHZ4D72/action/replication_record"}},"created_at":"2026-05-18T00:13:10.346536+00:00","updated_at":"2026-05-18T00:13:10.346536+00:00"}